2025 SIGIR Workshop on eCommerce
Overview
| Field | Content | |------|------| | Title | 2025 SIGIR Workshop on eCommerce | | Link | https://sigir-ecom.github.io/ | | Resource type | Conference / Workshop | | Section | Conferences, Workshops |
The 2025 SIGIR Workshop on eCommerce focuses on key problems and novel approaches in information retrieval for e-commerce, including large-scale search, recommendation, and personalization systems.
Background and Scope
Large-scale search, recommendation, and personalization systems have long faced challenges in efficiency, scalability, and user intent understanding. Traditional pipeline approaches often treat retrieval, ranking, and generation as separate stages, which struggles to meet users' needs in the LLM era for natural-language interaction, multi-hop reasoning, and up-to-date knowledge.
Core scenario coverage includes: open-domain information access, enterprise knowledge retrieval, conversational search, semantic understanding in recommendation systems, and end-to-end architectures that combine external knowledge sources with generative models.
Key Themes and Contributions
- A unified perspective that brings scattered related work into a comparable framework.
- A clear decomposition of method components (representation learning, retrievers, rerankers, planners, generators, feedback mechanisms) to ease engineering adoption.
- Reproducible benchmarks, datasets, or taxonomy tables from experimental protocols or survey coverage, lowering the entry cost for follow-up researchers.
- Discussion of interfaces with emerging paradigms such as LLM tool calling, reinforcement learning, and multi-agent collaboration, including migration paths from research prototypes to industrial systems.
- Explicit open problems: evaluation trustworthiness, latency and cost, hallucination and safety, and cross-lingual / multimodal extension.
- Activate
- CIKM 2024 1st Workshop on Multimodal Search and Recommendations
- EACL 2024 Workshop on Personalization of Generative AI Systems
- Haystack
- ICDM MMSR 2025
- KDD 2024 Workshop on Generative AI for Recommender Systems
- Source: 2025 SIGIR Workshop on eCommerce. See official link: https://sigir-ecom.github.io/
Typical Methodology Pipeline
Methods in this space generally follow four steps: problem formalization → model/system design → training/construction → inference pipeline.
1. Input and representation: encode queries, documents, and user context as dense or sparse representations, or construct structured prompts. 2. Core modules: retrievers, rerankers, planners, memory modules, tool interfaces — chained or parallelized per task. 3. Learning strategies: supervised fine-tuning, contrastive learning, distillation, reinforcement learning (including process rewards), bootstrapped data synthesis. 4. Inference strategies: single-turn retrieval, iterative retrieval, parallel sub-queries, early stopping and budget control.
Insights for Search / Recommendation / Personalization
1. Architecture: cascaded retrieve-rerank-generate remains mainstream, but the agentic paradigm is making "how many times and by what strategy to retrieve" itself a learnable object. 2. Data: high-quality instruction data is as critical as click/session logs; synthetic data must guard against knowledge leakage and distribution shift. 3. Evaluation: the gap between offline metrics and online satisfaction is widening; LLM-as-judge needs cross-validation with human evaluation. 4. Product: latency, cost, interpretability, and safety policy are hard constraints for industrial deployment — academic benchmarks alone are insufficient.
Engineering Checklist
| Item | Question | Suggestion | |--------|------|------| | Data | Does training/index data contain PII? How is it versioned? | Partitioned indices, anonymization, rollback-capable embedding versions | | Latency | What is the p99 budget? How many retrieval steps? | Cascading + early stopping, caching popular queries, async reranking | | Quality | Do offline gains translate to online CTR/satisfaction? | Interleaving experiments, manual audits, citation verification | | Safety | Does open retrieval introduce poisoning/bias? | Source whitelisting, adversarial detection, output filtering | | Cost | Token and GPU cost per query? | Routing to smaller models, distillation, hybrid sparse+dense |
Limitations
Common limitations in this area include experiment scale constrained by GPU budgets, benchmarks misaligned with real user distributions, English-centric data with unknown cross-lingual generalization, and safety risks of agentic systems on the open web. Future directions include more efficient test-time compute allocation, deeper integration with knowledge graphs and structured databases, and causal/fairness constraints for recommendation.